Virtual IR Sensing for Planetary Rovers: Improved Terrain Classification and Thermal Inertia Estimation

Yumi Iwashita, Kazuto Nakashima, Joseph Gatto, Shoya Higa, Adrian Stoica, Norris Khoo, Ryo Kurazume

研究成果: ジャーナルへの寄稿学術誌査読

7 被引用数 (Scopus)

抄録

Terrain classification is critically important for Mars rovers, which rely on it for planning and autonomous navigation. On-board terrain classification using visual information has limitations, and is sensitive to illumination conditions. Classification can be improved if one fuses visual imagery with additional infrared (IR) imagery of the scene, yet unfortunately there are no IR image sensors on the current Mars rovers. A virtual IR sensor, estimating IR from RGB imagery using deep learning, was proposed in the context of a MU-Net architecture. However, virtual IR estimation was limited by the fact that slope angle variations induce temperature differences within the same terrain. This paper removes this limitation, giving good IR estimates and as a consequence improving terrain classification by including the additional angle from the surface normal to the Sun and the measurement of solar radiation. The estimates are also useful when estimating thermal inertia, which can enhance slip prediction and small rock density estimation. Our approach is demonstrated in two applications. We collected a new data set to verify the effectiveness of the proposed approach and show its benefit by applying to the two applications.

本文言語英語
論文番号9158384
ページ(範囲)6302-6309
ページ数8
ジャーナルIEEE Robotics and Automation Letters
5
4
DOI
出版ステータス出版済み - 10月 2020

!!!All Science Journal Classification (ASJC) codes

  • 制御およびシステム工学
  • 生体医工学
  • 人間とコンピュータの相互作用
  • 機械工学
  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ サイエンスの応用
  • 制御と最適化
  • 人工知能

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